Financial Statement Fraud: Challenges and Technology Deployment in Fraud Detection

Intan Waheedah Othman

doi:10.5296/ijafr.v11i4.19067

Abstract

Fraudulent financial reporting and other forms of earnings misstatement are catastrophic and pose a considerable threat to capital market stability. This study reviews the literature on existing technology-based methods of detecting financial statement fraud. The aim is to describe the challenges of predicting a rare fraud event and provide an understanding of the various data-mining based techniques for financial statement fraud detection. Given that fraudsters are becoming more adaptable and are constantly devising new ways to outwit the fraud detection system, the study provides directions for future research in detecting the evolutionary fraudulent financial reporting.

Highlights

Fraud cases have significantly increased and continued to gain prominence
Fraudulent financial reporting detection is an important topic in accounting research
This study reviews literature on the importance, challenges, and the various statistical and computational intelligence approaches to detecting fraudulent reporting. Despite their differences in performance effectiveness, the literature review revealed that each technique is capable of detecting financial fraud

Summary

Introduction

Fraud cases have significantly increased and continued to gain prominence. Following the well-renown fraud cases such as Enron and WorldCom that had their earnings decreased by billions (Graham et al, 2008), recent cases such as Tesco, JP Morgan and Green Mountain Coffee have caused severe erosion of shareholder confidence in capital markets and drawn public attention to the criticality of fraud (Peterson & Buckhoff, 2004; Rezaee et al, 2004). The use of automated systems for detecting fraudulent financial reporting has gained increasing attention as a result of the application of computer-assisted mechanisms to commit fraud and the evolution of technologies used to evade fraud detection (SEC, 2019). These automated systems for fraud detection are critical, especially for auditors, since they enhance the pace and accuracy of auditing (Abbasi et al, 2012; Albrecht et al 2008). A faster and efficient fraud detection strategy can substantially reduce the magnitude and loss of fraud (ACFE, 2020) In addressing this issue, significant attempts have been undertaken to develop intelligent systems capable of detecting financial statement fraud. This paper discusses (1) the low predictability of rare events, (2) explores existing technology-based methods in financial statement fraud detection, and (3) suggests future research in detecting the evolutionary fraudulent financial reporting

The Low Predictability of Rare Fraud Event

Technology Deployment in Detecting Financial Fraud

Findings

Conclusion